Global Sustainable Energy Analysis Using Data-Driven Methods and Machine Learning Article Swipe
This study presents a comprehensive analysis of global sustainable energy trends using statistical techniques and machine learning methods. The research evaluates long-term patterns in renewable energy consumption, fossil fuel dependence, and the transition toward low-carbon systems from 1990 to 2022. Using a CRISP-DM data-science framework, the study performs data preparation, exploratory analysis, correlation assessments, regression modelling, and predictive analytics to understand the future trajectory of clean energy adoption. Two predictive machine-learning models—a Decision Tree Regressor and a Multi-Layer Perceptron Neural Network—were applied to forecast future energy consumption trends. Comparative evaluation shows that the Decision Tree model provides slightly higher accuracy for long-term forecasting, while the Neural Network captures non-linear patterns across regions more effectively. The results highlight increasing global adoption of solar, wind, and hydropower, while fossil fuel consumption continues to decline in most regions. The research also identifies major challenges, including regional inequalities in renewable access, policy gaps, and fluctuating economic conditions. Overall, the study concludes that a sustainable global energy transition is achievable through targeted policies, technological innovation, and integrated predictive modelling tools. This work provides an accessible reference for researchers, policymakers, and practitioners interested in renewable energy forecasting, global sustainability trends, and the application of machine-learning methods to environmental and energy datasets.
Related Topics
Concepts
Decision tree
Renewable energy
Energy consumption
Artificial neural network
Multilayer perceptron
Machine learning
Perceptron
Computer science
Artificial intelligence
Sustainability
Tree (set theory)
Environmental economics
Sustainable development
Energy transition
Big data
Energy (signal processing)
Work (physics)
Fossil fuel
Predictive analytics
Consumption (sociology)
Energy policy
Decision tree model
Predictive modelling
Ensemble learning
Heuristic
Environmental impact of the energy industry
Decision support system
Pruning
Data mining
Engineering
Exploratory data analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.17701542
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106499719
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106499719Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.17701542Digital Object Identifier
- Title
-
Global Sustainable Energy Analysis Using Data-Driven Methods and Machine LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-24Full publication date if available
- Authors
-
Rehman, HamzaList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17701542Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17701542Direct OA link when available
- Concepts
-
Decision tree, Renewable energy, Energy consumption, Artificial neural network, Multilayer perceptron, Machine learning, Perceptron, Computer science, Artificial intelligence, Sustainability, Tree (set theory), Environmental economics, Sustainable development, Energy transition, Big data, Energy (signal processing), Work (physics), Fossil fuel, Predictive analytics, Consumption (sociology), Energy policy, Decision tree model, Predictive modelling, Ensemble learning, Heuristic, Environmental impact of the energy industry, Decision support system, Pruning, Data mining, Engineering, Exploratory data analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.predictive | 57, 69, 172 |
| abstract_inverted_index.regression | 54 |
| abstract_inverted_index.techniques | 13 |
| abstract_inverted_index.trajectory | 63 |
| abstract_inverted_index.transition | 32, 162 |
| abstract_inverted_index.understand | 60 |
| abstract_inverted_index.Comparative | 88 |
| abstract_inverted_index.Multi-Layer | 77 |
| abstract_inverted_index.application | 196 |
| abstract_inverted_index.challenges, | 140 |
| abstract_inverted_index.conditions. | 152 |
| abstract_inverted_index.consumption | 86, 128 |
| abstract_inverted_index.correlation | 52 |
| abstract_inverted_index.dependence, | 29 |
| abstract_inverted_index.exploratory | 50 |
| abstract_inverted_index.fluctuating | 150 |
| abstract_inverted_index.hydropower, | 124 |
| abstract_inverted_index.innovation, | 169 |
| abstract_inverted_index.statistical | 12 |
| abstract_inverted_index.sustainable | 8, 159 |
| abstract_inverted_index.assessments, | 53 |
| abstract_inverted_index.consumption, | 26 |
| abstract_inverted_index.data-science | 43 |
| abstract_inverted_index.effectively. | 113 |
| abstract_inverted_index.forecasting, | 102, 190 |
| abstract_inverted_index.inequalities | 143 |
| abstract_inverted_index.preparation, | 49 |
| abstract_inverted_index.researchers, | 182 |
| abstract_inverted_index.comprehensive | 4 |
| abstract_inverted_index.environmental | 201 |
| abstract_inverted_index.policymakers, | 183 |
| abstract_inverted_index.practitioners | 185 |
| abstract_inverted_index.technological | 168 |
| abstract_inverted_index.Network—were | 80 |
| abstract_inverted_index.sustainability | 192 |
| abstract_inverted_index.machine-learning | 70, 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |